# Analyzing the Fast-Charging Potential for Electric Vehicles with Local Photovoltaic Power Production in French Suburban Highway Network

^{1}

^{2}

^{*}

## Abstract

**:**

## 1. Introduction

## 2. Literature Review

**Transportation-based**approaches focus on the transportation perspective when designing EV charging networks (i.e., mobility flows and passengers demand), while ignoring power system constraints [10]. Their main drawback is that they need to be readjusted according to the existing power system conditions. On the other hand,

**electric-based**approaches aim to locate EV charging stations in power systems such that their capacity and security requirements are satisfied and the investment costs needed to upgrade them are minimized [11] (i.e., transportation constraints are ignored). These approaches also need to be readjusted according to the existing mobility conditions. Since both transportation and electric perspectives are important in our case, we develop a

**hybrid**approach in this paper, where both types of constraints are considered [12]. We thus propose to deploy EV charging stations while taking into account both the actual transportation and power system conditions in the studied area.

**nodal demand-based**planning, charging stations are located to satisfy EV charging demands that appear at some geographical locations. The main drawback is that some transportation network issues (e.g., traffic congestion) are ignored in this methodology. On the other hand,

**simulation-based**and

**traffic flow-based**planning estimate EV charging demands based on real-life transportation surveys and origin-destination traffic flow (OD matrices), respectively. However, simulating EV charging demands is computationally expensive, while EV driving-range limit is not often considered when planning traffic flow-based methods. Relatively, EV charging demands can be represented in different ways [9]. In

**point-based**representations, EV charging demands are concentrated at certain points. In addition,

**polygon-based**representations consider dividing the studied area into smaller sub-areas (e.g., polygons) where each charging demand is represented by the centroid of the sub-area where it is located. The spatial attributes of the demand are thus ignored. Unlike the first two cases,

**network-based**representations do not only consider the spatial attributes of the demand; they also reflect the existing highway network as well as the different travel patterns of travelers in the studied area (commute, transit, etc.). In this latter case, charging demands can appear during long-distance or short-distance trips (referred to as

**inter-city**and

**intra-city**, respectively). As such, we use a traffic flow-based methodology in order to estimate EV charging demands where a network-based approach is used to represent them. This choice is made as it complies with our problem description and takes into account the availability of traffic flow data for the studied area.

**Level 1 chargers**(referred to as slow-charging, 110V/15A),

**Level 2 chargers**(220V/15-30A) and

**Level 3 chargers**(referred to as fast-charging, 400-500V/50A) (Table 1). These chargers have different features and requirements, such as power capacities, charging times, cabling and outlets, etc. Some studies considered only one type of chargers, while other studies integrated different types of chargers into their models. For example, in [9], the distribution of Level 2 charging stations among territory segments is considered, based on the potential use of EVs and the different parking behaviors. In [12], the focus is on selecting locations for fast-charging stations (Level 3) through a highway network for long-distance trips in the US. On the other hand, in [6], the deployment of both Level 2 and Level 3 chargers is considered to fulfil EV charging requirements while respecting the specifications of the electric grid. In this paper, we consider the deployment of Level 3 chargers as the aim is to satisfy charging demands on a highway network where EVs need to be recharged at short charging times.

**Facility Location Model (FLM)**[9] or a

**set-covering**model [14], while others introduced a

**Flow Refueling Location Model (FRLM)**or one of its variants (e.g., Multi-period FRLM, Capacitated FRLM, etc.) as they provide a better coverage of mobility flows [10]. These different formulations share many features and constraints but can also vary depending on the problem setting and its application context. Different objectives can be assigned to these models, such as

**minimizing investment costs**[15],

**maximizing covered mobility flows**or

**the number recharged EVs**[6] and

**minimizing$C{O}_{2}$emissions**[16]. The choice of which objective to use depends on the optimization problem itself and its overall aim.

**node-based modeling**) (e.g., EV charging demand in residential areas) [17]. Some other studies located charging demands through arcs linking different nodes (

**arc-based modeling**) (e.g., charging demand through a highway linking two residential areas) [10]. Furthermore, more researchers are recently interested in modeling charging demands using sequences of nodes and arcs (

**path-based modeling**) [18]. This combined approach has the advantage of modeling traffic flows where the aim is to locate charging stations so that the captured flow is maximized. In this paper, we consider a facility location model (FLM) where a path-based modeling approach is used in order to maximize the charging demand covered by the proposed network of fast-charging stations. Once the decision model is built, most studies suggested solving it using a

**mathematical solver**(e.g., Cplex, Gurobi or other solvers) so that the optimal deployment of EV charging stations can be found. However, due to the potential complexity of the underlying optimization problem, some studies developed more sophisticated methods for solving these models, such as

**Branch-and-Bound (B&B)**[13],

**Dynamic Programming (DA)**[15] and

**Genetic Algorithms (GA)**[14]. In this paper, we provide a data-driven framework in which we solve the proposed model using Cplex mathematical solver and analyze the obtained results. This is due to the limited number of variables in our model which allows finding solutions in feasible computational times (Section 4). Solving such models requires input data regarding different mobility and electric aspects. These datasets can either be based on real-life case studies (

**real datasets**, as in our case) or on datasets that are generated randomly or using a simulation approach (

**simulated datasets**). A comprehensive summary of the reviewed literature is presented in Table 2.

## 3. Problem Description

## 4. Modeling Framework

#### 4.1. Modeling Demand and Charging Network

#### 4.2. Modeling PV Power Production

- Fully-Sunny (where $\mu \left(t\right)$ is between 0% and 25%)
- Partially-Cloudy (where $\mu \left(t\right)$ is between 25% and 50%)
- Mostly-Cloudy (where $\mu \left(t\right)$ is between 50% and 75%)
- Fully-Cloudy (where $\mu \left(t\right)$ is between 75% and 100%)

#### 4.3. Mathematical Modeling

## 5. Results and Discussion

#### 5.1. Deploying EV Fast-Chargers

#### 5.2. Analyzing PV Power Integration

#### 5.3. Analyzing Energy Prices and Production Costs

## 6. Conclusions

## Author Contributions

## Funding

## Acknowledgments

## Conflicts of Interest

## References

- Mourad, A.; Puchinger, J.; Chu, C. A survey of models and algorithms for optimizing shared mobility. Transp. Res. Part B Methodol.
**2019**, 123, 323–346. [Google Scholar] [CrossRef] - Shareef, H.; Mainul Islam, M.D.; Mohamed, A. A review of the stage-of-the-art charging technologies, placement methodologies, and impacts of electric vehicles. Renew. Sustain. Energy Rev.
**2016**, 64, 403–420. [Google Scholar] [CrossRef] - Macioszek, E. E-mobility infrastructure in the Górnoslasko—Zaglebiowska Metropolis, Poland, and potential for development. In Proceedings of the World Congress on New Technologies, Lisbon, Portugal, 18–20 August 2019. [Google Scholar]
- Madhusudhanan, A.K.; Na, X.; Cebon, D. A Computationally Efficient Framework for Modelling Energy Consumption of ICE and Electric Vehicles. Energies
**2021**, 14, 2031. [Google Scholar] [CrossRef] - Jones, C.B.; Lave, M.; Vining, W.; Garcia, B.M. Uncontrolled Electric Vehicle Charging Impacts on Distribution Electric Power Systems with Primarily Residential, Commercial or Industrial Loads. Energies
**2021**, 14, 1688. [Google Scholar] [CrossRef] - Sun, Z.; Gao, W.; Li, B.; Wang, L. Locating charging stations for electric vehicles. Transp. Policy
**2018**, 98, 48–54. [Google Scholar] [CrossRef] - Fachrizal, R.; Shepero, M.; van der Meer, D.; Munkhammar, J.; Widén, J. Smart charging of electric vehicles considering photovoltaic power production and electricity consumption: A review. eTransportation
**2020**, 4, 100056. [Google Scholar] [CrossRef] - Deb, S.; Tammi, K.; Kalita, K.; Mahanta, P. Review of recent trends in charging infrastructure planning for electric vehicles. Wiley Interdiscip. Rev. Energy Environ.
**2018**, 7, e306. [Google Scholar] [CrossRef] [Green Version] - Csiszár, C.; Csonka, B.; Földes, D.; Wirth, E.; Lovas, T. Urban public charging station locating method for electric vehicles based on land use approach. J. Transp. Geogr.
**2019**, 74, 173–180. [Google Scholar] [CrossRef] - Capar, I.; Kuby, M.; Leon, V.; Tsai, Y. An arc cover–path-cover formulation and strategic analysis of alternative-fuel station locations. Eur. J. Oper. Res.
**2013**, 227, 142–151. [Google Scholar] [CrossRef] - Guo, Z.; Deride, J.; Fan, Y. Infrastructure planning for fast charging stations in a competitive market. Transp. Res. Part C Emerg. Technol.
**2016**, 68, 215–227. [Google Scholar] [CrossRef] - He, Y.; Kockelman, K.M.; Perrine, K.A. A Optimal locations of U.S. fast charging stations for long-distance trip completion by battery electric vehicles. J. Clean. Prod.
**2019**, 214, 252–461. [Google Scholar] [CrossRef] - Zhang, H.; Moura, S.J.; Hu, Z.; Song, Y. PEV Fast-Charging Station Siting and Sizing on Coupled Transportation and Power Networks. IEEE Trans. Smart Grid
**2018**, 9, 2595–2605. [Google Scholar] [CrossRef] - Vazifeh, M.M.; Zhang, H.; Santi, P.; Ratti, C. Optimizing the deployment of electric vehicle charging stations using pervasive mobility data. Transp. Res. Part A Policy Pract.
**2019**, 121, 75–91. [Google Scholar] [CrossRef] [Green Version] - Yi, Z.; Shirk, M. Data-driven optimal charging decision making for connected and automated electric vehicles: A personal usage scenario. Transp. Res. Part C Emerg. Technol.
**2018**, 86, 37–58. [Google Scholar] [CrossRef] - Liu, Q.; Liu, J.; Le, W.; Guo, Z.; He, Z. Data-driven intelligent location of public charging stations for electric vehicles. J. Clean. Prod.
**2019**, 232, 531–541. [Google Scholar] [CrossRef] - Acha, S.; van Dam, K.H.; Shah, N. Modelling spatial and temporal agent travel patterns for optimal charging of electric vehicles in low carbon networks. In Proceedings of the 2012 IEEE Power and Energy Society General Meeting, San Diego, CA, USA, 22–26 July 2012; IEEE: New York, NY, USA, 2012; pp. 1–8. [Google Scholar]
- MirHassani, S.A.; Ebrazi, R. A Flexible Reformulation of the Refueling Station Location Problem. Transp. Sci.
**2013**, 47, 617–628. [Google Scholar] [CrossRef] - Khan, F.A.; Pal, N.; Saeed, S.H. Review of solar photovoltaic and wind hybrid energy systems for sizing strategies optimization techniques and cost analysis methodologies. Renew. Sustain. Energy Rev.
**2018**, 92, 937–947. [Google Scholar] [CrossRef] - Ozcan, O.; Ersoz, F. Project and cost-based evaluation of solar energy performance in three different geographical regions of Turkey: Investment analysis application. Eng. Sci. Technol. Int. J.
**2019**, 22, 1098–1106. [Google Scholar] [CrossRef] - Good, C.; Shepero, M.; Munkhammar, J.; Boström, T. Scenario-based modelling of the potential for solar energy charging of electric vehicles in two Scandinavian cities. Energy
**2019**, 168, 111–125. [Google Scholar] [CrossRef] - Thomas, D.; Deblecker, O.; Ioakimidis, C.S. Optimal operation of an energy management system for a grid-connected smart building considering photovoltaics’ uncertainty and stochastic electric vehicles’ driving schedule. Appl. Energy
**2018**, 210, 1188–1206. [Google Scholar] [CrossRef] - Xi, X.; Sioshansi, R.; Marano, V. Simulation–optimization model for location of a public electric vehicle charging infrastructure. Transp. Res. Part D Transp. Environ.
**2013**, 22, 60–69. [Google Scholar] [CrossRef] - Chung, S.H.; Kwon, C. Multi-period planning for electric car charging station locations: A case of Korean Expressways. Eur. J. Oper. Res.
**2015**, 242, 677–687. [Google Scholar] [CrossRef] - Riemann, R.; Wang, D.Z.W.; Busch, F. Optimal location of wireless charging facilities for electric vehicles: Flow-capturing location model with stochastic user equilibrium. Transp. Res. Part C Emerg. Technol.
**2015**, 58, 1–12. [Google Scholar] [CrossRef] - Huang, K.; Kanaroglou, P.; Zhang, X. The design of electric vehicle charging network. Transp. Res. Part D Transp. Environ.
**2016**, 49, 1–17. [Google Scholar] [CrossRef] [Green Version] - Efthymiou, D.; Chrysostomou, K.; Morfoulaki, M.; Aifantopoulou, G. Electric vehicles charging infrastructure location: A genetic algorithm approach. Eur. Transp. Res. Rev.
**2017**, 9, 27. [Google Scholar] [CrossRef] [Green Version] - He, J.; Yang, H.; Tang, T.Q.; Huang, H.J. An optimal charging station location model with the consideration of electric vehicle’s driving range. Transp. Res. Part C Emerg. Technol.
**2018**, 86, 641–654. [Google Scholar] [CrossRef] - Chen, R.; Qian, X.; Miao, L.; Ukkusuri, S.V. Optimal charging facility location and capacity for electric vehicles considering route choice and charging time equilibrium. Comput. Oper. Res.
**2020**, 113, 104776. [Google Scholar] [CrossRef] - Diop, F. Probabilistic Load Flow Computation for Unbalanced Distribution Grids with Distributed Generation; Paris-Saclay University: Gif-sur-Yvette, France, 2018. [Google Scholar]
- Funke, S.Á.; Sprei, F.; Gnann, T.; Plötz, P. How much charging infrastructure do electric vehicles need? A review of the evidence and international comparison. Transp. Res. Part D Transp. Environ.
**2019**, 77, 224–242. [Google Scholar] [CrossRef] - Vlad, C.; Bancila, M.A.; Munteanu, T.; Murariu, G. Using renewable energy sources for electric vehicles charging. In Proceedings of the 2013 4th International Symposium on Electrical and Electronics Engineering (ISEEE), Galati, Romania, 11–13 October 2013; pp. 1–6. [Google Scholar]

Chrg. Level | Input V/A | Max. Power | Chrg. Time |
---|---|---|---|

Level 1 | 120VAC-20A (16A usable) | 1.92 kW (1-phase) | 10–13 h |

Level 2 | 400VAC-80A (64A usable) | 25.6 kW (3-phase) | 1–3 h |

Level 3 | 600VAC-200A (160A usable) | 96 kW (3-phase) | 0.2–0.58 h |

Reference | Discipline | Modeling Appr. | Planning Appr. | Model | Pattern | Demand Repr. | Objective | EV Charger | Solution Appr. | Data |
---|---|---|---|---|---|---|---|---|---|---|

Acha [17] | hybrid | node-based | demand | TCOPF | intra-city | point | min. cost | L2 | CPLEX | simulated |

Xi [23] | hybrid | node-based | flow | FLM | inter-city | polygon | max. EV charged | L1 and L2 | CPLEX | simulated |

Capar [10] | transport | arc-based | flow | FRLM | both | polygon | max. flow covered | NA | CPLEX | simulated |

MirHassani [18] | hybrid | path-based | flow | FRLM | intra-city | network | min. cost | NA | CPLEX | simulated |

Chung [24] | hybrid | path-based | flow | M-FRLM | inter-city | network | max. flow covered | L3 | B&B CPLEX | real |

Riemann [25] | hybrid | node-based | flow | FRLM | intra-city | point | max. flow covered | L2 | CPLEX | simulated |

Huang [26] | hybrid | node-based | demand | FC-GS | both | polygon | min. cost | L2 and L3 | CPLEX | real |

Guo [11] | hybrid | node-based | flow | MOPEC | intra-city | network | max. benefit | L3 | Gurobi | simulated |

Efthymiou [27] | transport | node-based | demand | NA | intra-city | point | min. No. of chargers | NA | GA | real |

Yi [15] | hybrid | path-based | demand | FLM | intra-city | point | min. cost | L2 and L3 | DA CPLEX | real |

Sun [6] | hybrid | path-based | flow | FLM | intra-city | point | max. flow covered | L2 and L3 | CPLEX | real |

He [28] | hybrid | path-based | flow | FRLM | intra-city | network | max. flow covered | L2 and L3 | CPLEX | simulated |

Zhang [13] | hybrid | node-based | simulation | CFRLM | inter-city | point | min. cost | L3 | B&B CPLEX | simulated |

Liu [16] | hybrid | node-based | demand | FLM | intra-city | network | min. emissions | L2 | PSOL | real |

He [12] | hybrid | path-based | flow | FRLM | inter-city | network | max. EV charged | L3 | MATLAB | real |

Csiszar [9] | hybrid | node-based | simulation | FLM | inter-city | polygon | max. flow covered | L2 | Bi-level CSL | real |

Chen [29] | transport | node-based | demand | FLM | inter-city | point | min. cost | L2 | Bi-level CSL | simulated |

Vazifeh [14] | transport | node-based | demand | set-covering | intra-city | polygon | min. No. of stations | L2 | Genetic algo. | real |

Our paper | hybrid | path-based | flow | FLP | both | network | max. flow covered | L3 | CPLEX | real |

Indices: | |
---|---|

$\mathcal{S}$ | Set of potential charging locations. |

$\mathcal{I}$ | Set of charging locations powered by distribution network. |

$\mathcal{V}$ | Set of charging locations powered by local PV station. |

$\mathcal{N}$ | Set of coupling nodes. |

$\mathcal{P}$ | Set of mobility paths. |

${\mathcal{S}}_{p}$ | Set of charging locations associated with path p. |

Parameters: | |

${\beta}^{a},{\beta}^{b}$ | Number of vehicles and trucks that can be recharged by a charger per day respectively. |

${q}^{a},{q}^{b}$ | Electric power needed to recharge a vehicle and a truck respectively. |

${c}^{a},{c}^{b}$ | Cost of installing a charger for vehicles and trucks respectively. |

For every charging location $s\in \mathcal{S}$: | |

${c}_{s}$ | Investment required to use location s as a charging station. |

${q}_{s},{\lambda}_{s}$ | Maximum electric capacity at location $s\in \mathcal{I}$ and $\mathcal{V}$ respectively. |

$mi{n}_{s}^{a},ma{x}_{s}^{a}$ | Min. and max. number of vehicle chargers that can be installed at location s. |

$mi{n}_{s}^{b},ma{x}_{s}^{b}$ | Min. and max. number of truck chargers that can be installed at location s. |

For every path $p\in \mathcal{P}$: | |

${d}_{p}^{a}$ | Charging demand for vehicles at path p. |

${d}_{p}^{b}$ | Charging demand for trucks at path p. |

Decision variables: | |

${x}_{s}=$ | $\left\{\begin{array}{cc}1\hfill & \mathrm{if}\phantom{\rule{4.pt}{0ex}}\mathrm{a}\phantom{\rule{4.pt}{0ex}}\mathrm{charging}\phantom{\rule{4.pt}{0ex}}\mathrm{station}\phantom{\rule{4.pt}{0ex}}\mathrm{is}\phantom{\rule{4.pt}{0ex}}\mathrm{deployed}\phantom{\rule{4.pt}{0ex}}\mathrm{at}\phantom{\rule{4.pt}{0ex}}\mathrm{location}\phantom{\rule{4pt}{0ex}}s\phantom{\rule{4pt}{0ex}}\hfill \\ 0\hfill & \mathrm{otherwise}\hfill \end{array}\right.$ |

${y}_{p}$ | Demand coverage rate at path p, ${y}_{p}\in [0,1]$. |

${z}_{a}^{s}$ | Number of vehicle chargers to be installed at location s. |

${z}_{b}^{s}$ | Number of truck chargers to be installed at location s. |

Path | Axes | n Nodes | s Nodes |
---|---|---|---|

${p}_{1}$ | A10 | ${n}_{3}$, ${n}_{5}$ | ${s}_{0}$, ${s}_{11}$, ${s}_{91}$ |

${p}_{2}$ | A10, A126 | ${n}_{5}$, ${n}_{13}$, ${n}_{6}$, ${n}_{7}$ | ${s}_{58}$, ${s}_{18}$, ${s}_{51}$, ${s}_{93}$, ${s}_{87}$, ${s}_{95}$ |

${p}_{3}$ | A6 | ${n}_{9}$, ${n}_{8}$, ${n}_{7}$ | ${s}_{23}$, ${s}_{48}$ |

${p}_{4}$ | N118, A10 | ${n}_{2}$, ${n}_{3}$, ${n}_{12}$ | ${s}_{81}$ |

${p}_{5}$ | N118 | ${n}_{12}$, ${n}_{4}$, ${n}_{10}$ | ${s}_{94}$, ${s}_{88}$, ${s}_{62}$, ${s}_{13}$, ${s}_{28}$, ${s}_{84}$ |

${p}_{6}$ | A126 | ${n}_{11}$, ${n}_{4}$, ${n}_{5}$, ${n}_{8}$ | ${s}_{35}$, ${s}_{79}$, ${s}_{3}$, ${s}_{77}$, ${s}_{33}$ |

${p}_{7}$ | N20 | ${n}_{1}$, ${n}_{15}$ | ${s}_{25}$, ${s}_{89}$, ${s}_{90}$, ${s}_{86}$ |

${p}_{8}$ | N20 | ${n}_{15}$, ${n}_{17}$, ${n}_{14}$ | ${s}_{83}$, ${s}_{92}$ |

${p}_{9}$ | N104 | ${n}_{1}$, ${n}_{2}$ | ${s}_{96}$, ${s}_{97}$ |

Winter | Spring | Summer | Autumn | |
---|---|---|---|---|

Fully-Sunny | 16.9% | 27.2% | 35.6% | 22.9% |

Partially-Cloudy | 6.7% | 9.1% | 15.6% | 9.1% |

Mostly-Cloudy | 11.4% | 17.6% | 21.5% | 11.6% |

Fully-Cloudy | 65.1% | 46.1% | 27.3% | 56.2% |

Parameter | Value | Parameter | Value |
---|---|---|---|

${c}^{a}$ | 12 k€ | ${c}^{b}$ | 15 k€ |

${\beta}^{a}$ | 36 vehicles | ${\beta}^{b}$ | 24 trucks |

${q}^{a}$ | 50 kW | ${q}^{b}$ | 250 kW |

Rate | Demand per Hour | No. of Chargers | Covered | Cost (€) | Extra (€) | ||
---|---|---|---|---|---|---|---|

No. of Vehicles | No. of Trucks | Vehicle | Truck | ||||

5% | 1985 | 218 | 255 | 72 | 31.1% | 4032K | - |

5% | 1985 | 218 | 683 | 111 | 100% | 9753 K | 5721 K |

10% | 3970 | 436 | 1456 | 218 | 100% | 19,914 K | 15,882 K |

20% | 7940 | 872 | 2650 | 436 | 100% | 38,380 K | 34,348 K |

No PV | PV-Wint. | PV-Spr. | PV-Sum. | PV-Aut. | |
---|---|---|---|---|---|

Overall | 33.1% | 37.3% | 40.9% | 44.4% | 39.1% |

A10 | 3.8% | 19.9% | 33.5% | 47.1% | 27.1% |

N104 | 8.3% | 29.9% | 53.3% | 74.2% | 39.8% |

10 Years | 15 Years | 20 Years | |
---|---|---|---|

Levelized Cost of Energy (LCOE) | 0.11 | 0.08 | 0.07 |

Return on Investment (ROI) | 148% | 224% | 260% |

Payback Period (PP) | 9.66 | 7.39 | 6.65 |

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. |

© 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).

## Share and Cite

**MDPI and ACS Style**

Mourad, A.; Hennebel, M.; Amrani, A.; Hamida, A.B.
Analyzing the Fast-Charging Potential for Electric Vehicles with Local Photovoltaic Power Production in French Suburban Highway Network. *Energies* **2021**, *14*, 2428.
https://doi.org/10.3390/en14092428

**AMA Style**

Mourad A, Hennebel M, Amrani A, Hamida AB.
Analyzing the Fast-Charging Potential for Electric Vehicles with Local Photovoltaic Power Production in French Suburban Highway Network. *Energies*. 2021; 14(9):2428.
https://doi.org/10.3390/en14092428

**Chicago/Turabian Style**

Mourad, Abood, Martin Hennebel, Ahmed Amrani, and Amira Ben Hamida.
2021. "Analyzing the Fast-Charging Potential for Electric Vehicles with Local Photovoltaic Power Production in French Suburban Highway Network" *Energies* 14, no. 9: 2428.
https://doi.org/10.3390/en14092428